Using Integrated Data Analytics Platforms to Destroy Silos
October 1, 2022

Using Integrated Data Analytics Platforms to Destroy Silos

Silos hinder an organization’s ability to accomplish its larger goals or, at the very least, postpone them. Silos have mostly been a problem for businesses of all sizes, regardless of how big or little they are. Silos continue to exist despite our knowledge of the damage they may do and the benefits of avoiding them.

Any firm may benefit greatly from an integrated platform for data analytics. The objective is for all departments in an organisation to be able to see and access data from various departments. It enables cross-departmental collaboration and communication by enabling one department to access and work on data produced by another department, minimising duplication and repetition of effort while boosting efficiency.

Describe a silo.

A department’s data that is partially hidden or inaccessible to other departments within the same corporation is referred to as a “silo.” The complete opposite of integration is siloing.

Consider this in the context of other organisational departments, such as HR, Marketing, Sales, Finance, and Administration. Each department strives to achieve its own functional objectives while also working toward the overall organisational goals. Now, if each of these functional divisions were to keep its own data independently, data silos would result.

As time goes on and more data is added to them, these silos tend to expand. Because the many departments are not linked to one another, this lack of communication between the departmental data is the ideal reason.

Additionally, there is a good probability of having duplicate work owing to the departments’ isolation, which would result in a waste of time and money. Therefore, the presence of silos as a whole has a pretty negative effect on the company and prevents it from fulfilling its goals.

What Leads to a Structure with Siloes?

Before Big Data became popular, different organisational departments were often urged to maintain their own data. The phrase “each to their own” was an appropriate way to see things since each department has its own operating procedures, regulations, and rules. This was one of the main drivers for the development of silos.

Organizational structure – Each department within an organisation has its own structure, procedure, and policies. As a result, they utilised to handle their own data according to their own unique needs. Data silos were thus created automatically. Additionally, it was never seen as an issue. With the advent of Big Data, cloud computing, and analytics, greater insight is now more important than ever. Business is thus increasingly focused on breaking down data silos and gathering insightful data for future development.

Departments are used to working in their own worlds as a result of business culture, which is linked to organisational structure. They operate differently from other departments due to their unique issues and working methods, which may have an impact on their statistics. Additionally, agencies have seldom been urged to combine their data since there hasn’t always been a clear necessity.

Technology – Many of the legacy technologies that businesses often use were not designed to facilitate data sharing. Departments have been forced to create and maintain data silos as a result of the usage of such technologies and carrying on with business operations “the way it has always been done.”

Scalability: Data silos may result from organisational growth and change. What works for a start-up with a small staff won’t apply to a scale-up or an exponentially growing company. Data sharing must be done in a different manner if a firm transitions from having one individual perform a job to having a team or department handle those obligations.

Why Do Data Silos Threaten Organizational Goals?

Business has been fueled by competition and the quest for profitability. It’s crucial to save expenses and make the most of their data resources. The precise things that prevent such usage, however, are data silos.

Silos prohibit data from being shared across departments, which implies that each department’s analysis is constrained by its own vision. This prevents any enterprise-wide inefficiencies from being found.

Threat to data integrity: Siloed data is kept in many databases, which may lead to the availability of inconsistent and erroneous data.

Resource waste – The existence of silos is a resource waste. The resources needed to store duplicate data, maintain it, and retrieve it may be a hassle in addition to using up resources that might be utilised more effectively elsewhere.

Collaboration across divisions within an organisation is discouraged by data silos since there is no data exchange involved. Data-driven businesses depend on the integration of data to get insightful knowledge that helps them expand their operations.

Destruction of Silos

Silos may be eliminated using both organisational and technological means. Integrated data analytics solutions have become available with the rise of the cloud, assisting businesses in making the most of their data. These platforms also save time and make good use of available resources.

Organizational Culture Modification

Since organisational culture contributes to the development of silos, it also holds the key to their elimination. The management level’s encouragement of data sharing has the potential to fundamentally alter how workers see data sharing. Effective communication is necessary to ensure that the benefits of data integrity are also integrated into workers’ regular work routines.

Data centralization

The easiest way to have all the data in one location is to combine corporate data from many departments into a cloud-based data warehouse. The process of expedited analysis will be helped by this single source. Diverse data may be unified and integrated in this way.

Data Integration

The greatest method to dismantle silos and stop them from emerging in the first place is via precise and efficient data integration. This kind of work may be done by:

Scripting

To transport data from siloed sources into warehouses, scripts written in scripting languages like Python may be delegated to IT departments in businesses. The drawback of this procedure is that it may quickly grow to be quite complicated. The number of data sources is increasing, which increases complexity and, therefore, the time and money required of IT specialists.

using regional ETL tools

Data movement from sources to the data warehouse is automated using ETL (extract, transform, and load) technologies. This is carried out locally by converting and transporting data from numerous sources to the organization’s data centre.

ETL software on the cloud

Data and the cloud often work well together, and these days, many cloud-based providers also provide speedier ETL procedures. ETL tools are effectively made to function in such a setting by using the infrastructure and knowledge of the service provider. They provide an integrated solution free of problems with data integrity as well as a simplified method for data analysis.

Using Integrated Data Analytics Solutions to Break Up Data Silos

There are many businesses that provide integrated data analytics as a solution for big, mid-sized, and small businesses since the cloud has developed into a natural area for the centralization of data. These are mostly helpful to businesses that do not have the internal resources to manually eliminate silos.

One of the most well-known services ever is called Snowflake. They effectively refer to their business as “data warehouse as a service.” Businesses may store and analyse their data on the cloud.

Another well-known provider that supports on-premises, hybrid, and multi-cloud architectures is Cloudera. Over a secure connection, insights are obtained using machine learning and analytics.

The Spark developers established Databricks, a product that is making some noise. Projects like Delta Lake, MLflow, and Koalas tackle the machine learning, data science, and data engineering fields. A web-based platform from Databricks integrates with Spark.

One of the most widely used tools for cloud data centralization is Talend Data Fabric. The ETL process, data governance, compliance, and security are all made simpler. Users using Talend data fabric may work together and break down departmental silos.

An “Integration Platform as a Service (iPaaS)” programme is Mulesoft. It is the alternative method for integrating high-quality data. Additionally, it enables the automated upload of data from various sources.

Conclusion

In many businesses, there are many distinct types of data silos. In the past, it wasn’t seen as a concern. But with the advent of big data and cloud, it is crucial to break down data silos and quickly and efficiently extract business insights.

The chance to develop is greater the better the understanding. As a consequence, firms are increasingly focused on data integration and accelerating growth.

It is now much simpler for us to permanently end data silos thanks to data integration technologies and cloud-based solutions.

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